There is an increasing utilisation of Artificial Neural Networks (ANN) in modern society. In the field of forensic DNA profile analysis, the use of ANNs to aid in the classification of peak data within casework electropherograms (EPG) has recently been described. Prior to implementation, an ANN must be created, trained, and adjusted until an acceptable level of performance is obtained. The training of an ANN for the classification of DNA profiles is undertaken using a supervised learning approach. In principle, DNA profile data manually labelled by an analyst comprise a training dataset where the ANN learns the features of EPG data and discerns the ‘correct’ type of peak classification to be assigned. Various factors of the training regimen impact the overall performance of the ANN. These include: the number of epochs (training cycles), the size of the training (and validation) dataset, the types of EPG data used for training, and the composition (different types of profiles) of the training dataset. We present how trained ANNs can assist laboratories streamline the designation of forensic DNA profiles. We describe the importance of the training regimen and how performance metrics can be used to validate the ANN and give examples for DNA profiles generated using the PowerPlex® Fusion 6C profiling kit.
There is an increasing utilisation of Artificial Neural Networks (ANN) in modern society. In the field of forensic DNA profile analysis, the use of ANNs to aid in the classification of peak data within casework electropherograms (EPG) has recently been described. Prior to implementation, an ANN must be created, trained, and adjusted until an acceptable level of performance is obtained. The training of an ANN for the classification of DNA profiles is undertaken using a supervised learning approach. In principle, DNA profile data manually labelled by an analyst comprise a training dataset where the ANN learns the features of EPG data and discerns the ‘correct’ type of peak classification to be assigned. Various factors of the training regimen impact the overall performance of the ANN. These include: the number of epochs (training cycles), the size of the training (and validation) dataset, the types of EPG data used for training, and the composition (different types of profiles) of the training dataset. We present how trained ANNs can assist laboratories streamline the designation of forensic DNA profiles. We describe the importance of the training regimen and how performance metrics can be used to validate the ANN and give examples for DNA profiles generated using the PowerPlex® Fusion 6C profiling kit.
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Senior Scientist, The New Zealand Institute for Public Health and Forensic Science Limited (PHF Science) (Formerly ESR)
Meng-Han Lin is a Senior Scientist at the Institute of Environmental Science and Research (ESR) and has been employed in the STRmix™ team since 2016. A molecular biologist by background, his role is primarily responsible for the development, validation, and support of FaSTR™ DNA analysis software.
